Compositional Learning Through Decision Tree Growth Processes and A Communication Protocol

Abstract

A communication protocol is disclosed that enables multiple decision tree forest modules arranged in a compositional network to grow in a coordinated way so as to reduce error as measured by an arbitrary classification process utilizing spike encodings from any of the decision trees forest modules. The disclosed solution to compositional machine learning is agnostic to both the hardware methodology used to implement it, as well as the local decision processes that power nodes in the decision trees. Any number of computing systems based on different technologies and physical arrangements can be built that will coordinate in solving arbitrary compositional learning problems, so long as the communication protocol is enforced.

Dependencies are inferred from the claim text.